#include "darknet_internal.hpp"


namespace
{
	static auto & cfg_and_state = Darknet::CfgAndState::get();
}


Darknet::Layer make_sam_layer(int batch, int index, int w, int h, int c, int w2, int h2, int c2)
{
	TAT(TATPARMS);

	*cfg_and_state.output << "scale Layer: " << index << std::endl;
	Darknet::Layer l = { (Darknet::ELayerType)0 };
	l.type = Darknet::ELayerType::SAM;
	l.batch = batch;
	l.w = w;
	l.h = h;
	l.c = c;

	l.out_w = w2;
	l.out_h = h2;
	l.out_c = c2;
	assert(l.out_c == l.c);
	assert(l.w == l.out_w && l.h == l.out_h);

	l.outputs = l.out_w*l.out_h*l.out_c;
	l.inputs = l.outputs;
	l.index = index;

	l.delta = (float*)xcalloc(l.outputs * batch, sizeof(float));
	l.output = (float*)xcalloc(l.outputs * batch, sizeof(float));

	l.forward = forward_sam_layer;
	l.backward = backward_sam_layer;
#ifdef DARKNET_GPU
	l.forward_gpu = forward_sam_layer_gpu;
	l.backward_gpu = backward_sam_layer_gpu;

	l.delta_gpu =  cuda_make_array(l.delta, l.outputs*batch);
	l.output_gpu = cuda_make_array(l.output, l.outputs*batch);
#endif
	return l;
}

void resize_sam_layer(Darknet::Layer *l, int w, int h)
{
	TAT(TATPARMS);

	l->out_w = w;
	l->out_h = h;
	l->outputs = l->out_w*l->out_h*l->out_c;
	l->inputs = l->outputs;
	l->delta = (float*)xrealloc(l->delta, l->outputs * l->batch * sizeof(float));
	l->output = (float*)xrealloc(l->output, l->outputs * l->batch * sizeof(float));

#ifdef DARKNET_GPU
	cuda_free(l->output_gpu);
	cuda_free(l->delta_gpu);
	l->output_gpu = cuda_make_array(l->output, l->outputs*l->batch);
	l->delta_gpu = cuda_make_array(l->delta, l->outputs*l->batch);
#endif

}

void forward_sam_layer(Darknet::Layer & l, Darknet::NetworkState state)
{
	TAT(TATPARMS);

	int size = l.batch * l.out_c * l.out_w * l.out_h;
	//int channel_size = 1;
	float *from_output = state.net.layers[l.index].output;

	int i;
	#pragma omp parallel for
	for (i = 0; i < size; ++i) {
		l.output[i] = state.input[i] * from_output[i];
	}

	activate_array(l.output, l.outputs*l.batch, l.activation);
}

void backward_sam_layer(Darknet::Layer & l, Darknet::NetworkState state)
{
	TAT(TATPARMS);

	gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
	//axpy_cpu(l.outputs*l.batch, 1, l.delta, 1, state.delta, 1);
	//scale_cpu(l.batch, l.out_w, l.out_h, l.out_c, l.delta, l.w, l.h, l.c, state.net.layers[l.index].delta);

	int size = l.batch * l.out_c * l.out_w * l.out_h;
	//int channel_size = 1;
	float *from_output = state.net.layers[l.index].output;
	float *from_delta = state.net.layers[l.index].delta;

	int i;
	#pragma omp parallel for
	for (i = 0; i < size; ++i) {
		state.delta[i] += l.delta[i] * from_output[i]; // l.delta * from  (should be divided by channel_size?)

		from_delta[i] = state.input[i] * l.delta[i]; // input * l.delta
	}
}

#ifdef DARKNET_GPU
void forward_sam_layer_gpu(Darknet::Layer & l, Darknet::NetworkState state)
{
	TAT(TATPARMS);

	int size = l.batch * l.out_c * l.out_w * l.out_h;
	int channel_size = 1;

	sam_gpu(state.net.layers[l.index].output_gpu, size, channel_size, state.input, l.output_gpu);

	activate_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation);
}

void backward_sam_layer_gpu(Darknet::Layer & l, Darknet::NetworkState state)
{
	TAT(TATPARMS);

	gradient_array_ongpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);

	int size = l.batch * l.out_c * l.out_w * l.out_h;
	int channel_size = 1;
	float *from_output = state.net.layers[l.index].output_gpu;
	float *from_delta = state.net.layers[l.index].delta_gpu;


	backward_sam_gpu(l.delta_gpu, size, channel_size, state.input, from_delta, from_output, state.delta);
}
#endif
